Label_Prop_sto_r: Computes a classification on the target data

In CytOpT: Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation

Description

Computes a classification on the target data thanks to the approximation of the transport plan and the classification of the source data. Transport plan is approximated with the stochastic algorithm.

Usage

Label_Prop_sto_r(
  X_s,
  X_t,
  Lab_source,
  eps = 1e-04,
  const = 0.1,
  n_iter = 4000,
  minMaxScaler = TRUE,
  monitoring = TRUE,
  thresholding = TRUE
)

Arguments

X_s	a cytometry dataframe. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The classification of this Cytometry data set must be provided with the Lab_source parameters.
X_t	a cytometry dataframe. The columns correspond to the different biological markers tracked. One line corresponds to the cytometry measurements performed on one cell. The CytOpT algorithm targets the cell type proportion in this Cytometry data set
Lab_source	a vector of length n Classification of the X_s cytometry data set
eps	an float value of regularization parameter of the Wasserstein distance. Default is 1e-04
const	an float constant. Default is 1e-01
n_iter	an integer Constant that iterate method select. Default is 4000
minMaxScaler	a logical flag indicating to possibly Scaler
monitoring	a logical flag indicating to possibly monitor the gap between the estimated proportions and the manual gold-standard. Default is FALSE
thresholding	a logical flag.

Value

a ggplot object

a vector of length nrow(X_t) with the propagated labels

Examples

if(interactive()){

res <- Label_Prop_sto_r(X_s = HIPC_Stanford_1228_1A, X_t = HIPC_Stanford_1369_1A, 
             Lab_source = HIPC_Stanford_1228_1A_labels)

}

CytOpT documentation built on Feb. 10, 2022, 1:07 a.m.